Author(s): Eisaku Yura; Kohji Tanaka; Yeonjoong Kim; Hiroki Tsujikura; Tatsuya Yoshida; Shigeho Maeda; Mayuko Takeichi; Ryosuke Azuma
Linked Author(s): Kohji Tanaka
Keywords: Deep learning; Deep neural network; Disaster prevention support system; Similar typhoon researching system
Abstract: We present a method for improving the search accuracy of the similar typhoon research system by leveraging deep neural networks, which are an extension of artificial neural networks. To the search engine, we apply three parameters of typhoons: course, temporal central pressure, and speed. We show that these parameters can improve accuracy when searching for past typhoons having characteristics similar to the target. Furthermore, the accuracy of functions designed to support the expected disaster prevention actions and flood fighting services was assessed based on the results from the search system.
Year: 2018